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Journals / Journal of Turkish Operations Management / 2023 / Cilt: 7 Sayı: 2

Data driven approach for weight restricted data envelopment analysis models with single output

Şenol Kurt(ATILIM UNIVERSITY, INSTITUTE OF SOCIAL SCIENCES)0000-0002-4526-1592
Mustafa Kerem Yüksel0000-0002-7051-6526
Burcu Dinçergök(ATILIM ÜNİVERSİTESİ, İŞLETME FAKÜLTESİ, İŞLETME BÖLÜMÜ)0000-0002-7050-8163
Pages
1768–1779
Publication Date
2023-12-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to explore whether a machine learning algorithm can be used to make improvements in assessing unit efficiencies via a data envelopment analysis (DEA) model.
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Key Findings & Data:In this study, a DEA model is used to calculate the efficiency scores of Desicion Making Units (DMUs).
💡
Conclusion & Impact:As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:DEA · ANN · DEA

Abstract

This study aims to explore whether a machine learning algorithm can be used to make improvements in assessing unit efficiencies via a data envelopment analysis (DEA) model. In this study, a DEA model is used to calculate the efficiency scores of Desicion Making Units (DMUs). Then, an ML algorithm is trained that aims to predict the single output using inputs. Ranking of input features based on relative feature importance values obtained from the trained ML model is fed to the DEA model as weight restrictions. As a result, the two DEA models are compared with each other. ML-based insights (feature importance ranking) improve the DEA model in the direction of fewer zero weights. The additional weight restrictions are data depdendent, and hence realistic. As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model.

Keywords
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Cite This Article

Kurt, Ş., Yüksel, M. K., Dinçergök, B. (2023). Data driven approach for weight restricted data envelopment analysis models with single output. Journal of Turkish Operations Management, Cilt: 7 Sayı: 2, 1768–1779. https://doi.org/10.56554/jtom.1333333

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